Support vector machines (SVMs) belong to a class of classifiers known as linear classifiers. An SVM may be trained using a set of images having known classifications. Each image has an associated set of features, which may be represented as a so-called “feature vector” in an n-dimensional space. Based on the feature vectors and known classifications for a set of images, the SVM determines one or more separating hyperplanes in the n-dimensional space that maximizes the margin(s) and/or distance(s) between subsets of the images having different classifications. When a previously unclassified image is to be classified, features of the image are extracted (e.g., calculated and/or otherwise determined), a corresponding feature vector is formed based on the image features, and the SVM classifies the image by determining, based on the separating hyperplane(s), in which portion of the n-dimensional space the feature vector is located. Example deterministic image features include, but are not limited to, brightness, color, position, feature size, edge strength, and/or edge direction. Such deterministic image features are computed using purpose-built and/or purposefully designed algorithms and/or methods that extract particular and/or definable characteristics of an image. Example deterministic features of an image of a face include, but are not limited to, a distance between the eyes, a dimension of an area defined by the eyes and nose, a dimension of a mouth, etc.